class_docstr: The model averaging is based on paper
  "Stochastic Gradient Descent for Non-smooth Optimization: Convergence
  Results and Optimal Averaging Schemes",
  (http://proceedings.mlr.press/v28/shamir13.pdf), which implements
  polynomial-decay averaging, parameterized by eta. When eta = 0, it is
  equivalent to simple average over all iterations with same weights.
docstr: Parameters
  ----------
  model
      The model that the cached model is loaded to.
docstr_tok: ["Parameters","--","--","--","--","--","model","The","model","that","the","cached","model","is","loaded","to","."]
code: def load_cached_model(self, model: nn.HybridBlock):
      r"""
      Parameters
      ----------
      model
          The model that the cached model is loaded to.
      """
      if self.cached_model is not None:
          # load the cached model
          for name, param_cached in self.cached_model.items():
              model.collect_params()[name].set_data(param_cached)
  
imports: from typing import Any, Dict, List, Optional
  import logging
  import mxnet
  import mxnet.gluon.nn
  from mxnet import gluon
  from gluonts.core.component import validated
  from .callback import Callback
label_desc: Use image node_id 6 for calling the IterationAveragingStrategy obj's underlying member method code with example usage: obj.load_cached_model(model) without return types